Computational Biology Research Center[CBRC]

The Biological Network Team develops technologies for biological network analysis, for application to actual issues in biology and medicine, especially the support of drug discovery and the prediction of adverse drug reactions. We first develop technologies for the effective utilization of biological information, the inference of biological network structure, and the analysis of network dynamics. We then combine the developed technologies into a pipeline for high-throughput network analysis. We also modify them for practical applications to biological and medical issues, especially for adverse drug reactions based on the inference of disease networks.

Research Topics

■Inference of biological network structure

We develop technologies for the inference of network structure from the information in the published literature and the measured data, and for the evaluation of consistency between the known networks based on the literature and the measured data of the constituent molecules in the network. In particular, we apply the technologies to infer disease-related networks, and to support the prediction of adverse drug reactions.

■Analysis of biological network dynamics

We develop network dynamics analysis technologies based on both standard optimization and computer algebra methodologies. The technology based on computer algebra (algebraic biology) intends to analyze the dynamics of large-scale networks from a small volume of measurement data, a situation that emerges frequently in biological experiments.

■Establishment of a biological network analysis platform

We develop a technological framework for the processing knowledge of pathway information and the promotion of international standards, and for value-additive pipeline integrating tools for effective data entry, network structure inference, and network dynamics analysis.

Team Member

Katsuhisa Horimoto

Leader E-mail

website

Key Words

Statistical inference for association and causality, Algebraic computation

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Papers List

• ■ Aburatani, S., Saito, S., Toh, H. and Horimoto, K.: A graphical chain model for inferring regulatory system networks from gene expression profiles. Statistical Methodology, 3, 17-28, (2006).
• ■ Aburatani, S., Goto, K., Saito, S., Toh, H. and Horimoto, K. ASIAN: A Web Server for Inferring a Regulatory Network Framework from Gene Expression Profiles. Nucleic Acids Res., 33, W659-W664, (2005).
• ■ Anai, H. and Horimoto K. (ed): Algebraic Biology 2005 - Computer Algebra in Biology. Universal Academy Press, Tokyo, (2005).
• ■ Tominaga, D. and Horton, P., Inference of Scale-free Networks From Gene Expression Time Series. J. Bioinfo. Comput. Biol., 503-514, (2006).
• ■ Yamamoto, S., Asanuma, T., Takagi, T. and Fukuda, K., An Ontology for Annotation of Signal Transduction Pathway Molecules in the Scientific Literature: Molecule Role Ontology. Comp. Funct. Genom. 5, 528-536, (2004).

Research Collaboration Partner

• ■ University, College, & Research Institute
  • Kanazawa University
  • Kyoto University
• ■ Company
  • Ajinomoto Co., Inc.